A0189
Title: Gradient synchronization for multivariate functional data, with application to brain connectivity
Authors: Yaqing Chen - Rutgers University (United States) [presenting]
Shu-Chin Lin - University of California Davis (United States)
Yang Zhou - UC Davis (United States)
Owen Carmichael - Pennington Biomedical Research Center (United States)
Jane-Ling Wang - University of California Davis (United States)
Hans-Georg Mueller - University of California Davis (United States)
Abstract: Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure of functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, gradient synchronization measures are introduced in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. The asymptotic normality of the proposed estimates is obtained under regularity conditions. The proposed synchronization measures are illustrated via simulations and an application to resting-state fMRI signals from the Alzheimer's disease neuroimaging initiative (ADNI), and they are found to improve discrimination between subjects with different disease statuses.
